1. Field of the Invention
The present invention relates to a weather prediction system and power demand prediction system, and a weather prediction method and power demand prediction method which predict a meteorological phenomenon and a power demand on the basis of the relationship between meteorological phenomena and power demands.
2. Description of the Related Art
Conventionally, prediction of meteorological phenomena is done independently of prediction of power demands. In recent years, atmospheric simulation software called a numerical weather prediction model. By using this software, meteorological phenomena are predicted several hours to about one week in the future. On the other hand, power demands show a strong correlation with atmospheric temperatures and humidities. For this reason, a power demand at its peak is predicted on the basis of the meteorological phenomenon prediction result (e.g., Jpn. Pat. Appln. KOKAI Publication No. 2003-180032).
When considering the relationship between meteorological phenomena and power demands, the heat-island phenomenon caused by rapid urbanization is nonnegligible. Major causes for the heat-island phenomenon are supposed to be the evaporation reduction effect (evaporation/transpiration decreases due to painting and the like, and cooling by heat of vaporization is suppressed) and artificial exhaust heat in the urban area (“Meteorological Science Encyclopedia”, edited by Meteorological Society of Japan, Tokyo Shuppan, p. 445). Some numerical weather prediction models take the former evaporation reduction effect into consideration for calculation. However, no attempt has been made to estimate the latter artificial exhaust heat and reflect it on prediction of meteorological phenomena.
Actually, however, a cycle that raises the temperature is supposed to occur. That is, when the temperature rises, the power consumption by air conditioners increases. The exhaust heat from the air conditioners raises the temperature and further invokes the power demand. If this cycle is left out of consideration, an abrupt increase in temperature may be underestimated. An abrupt increase in temperature causes local severe rain at a high probability. Hence, the underestimation adversely affects prediction of local severe rain and makes it difficult to predict flood risk. It may also lead to degradation in prediction accuracy of the power demand which is calculated on the basis of meteorological phenomena.